A Better Defensive Line

How U.S. agencies are working toward a more appropriate security line

By John Merlino

Oct 01, 2017

If I asked you to name the greatest defensive line of all time,
what immediately pops into your head? The Steel Curtain of the
Pittsburgh Steelers? Maybe the Denver Broncos Orange Crush?
While these defensive linemen were certainly legendary, we all
recognize that they weren’t infallible. It ultimately required a
team effort to win the day.

The same is true when it comes to border security. When the ancient
Chinese constructed the Great Wall of China—an architectural
feat that stretched across more than 13,000 miles of rugged country
and steep mountains—the wall was by no means their only line
of defense. The provinces also relied on the protection of a highly
skilled military force. Fast forward 2,300 years and border security
in countries like the United States not only rely on highly trained law
enforcement agencies and physical barriers, but a sophisticated array
of technologies designed to prevent unlawful entry.

In the United States, technology has become the essential force
multiplier. With nearly 6,000 miles of border with the neighboring
north and south, it would be impossible to hire enough border
personnel to protect every mile. So in addition to fences, walls and
vehicle blockades, the U.S. increasingly relies on a strategic mix of
technologies, sensors, radar and thermal detection, biometrics, and
video analytics to safeguard the integrity of our borders.

Deploying Networked Sensors
and Detectors

Post 9/11, the Department of Homeland Security (DHS) has more
than tripled the number of Border Patrol agents along U.S. northern
and southern frontiers. They’ve also greatly increased the number of
sensors embedded in roads and wooded areas near crossings to detect
vehicles and people attempting to circumvent legitimate points of entry.
Depending on how they’ve been programmed, they can trigger
different actions—sirens, floodlights, loudspeaker announcements,
video recording and alerts to border patrols.
Some of the sensor and detector technology you can find currently
being deployed at U.S. borders are:

Passive-infrared (PIR). PIR sensors sense the movement of things
that radiate heat—people, animals, vehicles and other objects. The
typical detection range for PIR sensors is relatively short—approximately
33 feet (10 meters). That’s about one-tenth of the length of a
football field or about half the length of a bowling alley lane.

Thermal. Thermal detectors capture the heat signature of objects
providing border security with sufficient detail to discern the
difference between people, animals and things. Their detection
range is considerably longer than PIR, though the field of view is
somewhat narrower.

Radar. Designed primarily to protect
against ground targets, radar detectors analyze
the electromagnetic pulses bouncing off
objects to determine their position, distance
and velocity, as well as the direction of their
movement (towards or away from the radar).
Mid-range radar detectors can spot a moving
target up to 164 feet (50 meters) away.
That’s almost half the length of a football
field. Long-range radar detectors can distinguish
a walking target up to three-quarters
of a mile away.

Another notable thing about radar technology
is that it can detect multiple targets
simultaneously. The more advanced models
can also provide important GIS data about
the target. Geographic Information System
(GIS) data is three dimensional in nature,
encompassing latitude and longitude, speed
and distance. If required, it can also provide
the target’s altitude or elevation.

Drone detection radar. With the increasing
incidents of drug traffickers using
drones to drop ship contraband across the
border, DHS is beginning to explore radar
technology specifically designed to recognize
and detect and small drones up to twothirds
of a mile in the sky. They continuously
monitor the drone’s changing GPS
coordinates and can transmit that data to
video cameras to assist in the tracking of
the object’s movement.

Generally these technologies can only detect
an object of interest. But when they’re
augmented with video they can be interrogated
to provide recognition and identification
affording operators true situational
awareness of their environment.

Tying in Surveillance Cameras,
Video Analytics and
Biometrics

If you think of sensors and radar technology
as the first line of defense, consider surveillance
cameras and analytics as the secondary.
Sensors provide the early warning or
detection. Cameras, now enhanced with an
array of advanced features, provide the critical
verification, identification and recognition.
Here, too, DHS avails itself of a wide
portfolio of tools in order to remain adaptive
to evolving threats.

High-resolution network video cameras.
Because our borders encompass diverse
terrain—from wetlands, grasslands, rivers
and mountain to forests, deserts and urban
sprawl—no one type of surveillance camera
is ideally suited to monitoring every kind of
environment. So a strategic mix is essential.
That’s why you’ll find a mix of fixed cameras
with variable focal lengths, PTZ cameras to
track suspicious movement, cameras with
wide and 360-degree fields of view, thermal
cameras that can detect people and objects
in darkness, fog and rain and even camouflaged
in the background, cameras with wide
dynamic range that provide critical details in
both shadow and bright sunlight, day/night
cameras, even lowlight-sensitivity cameras
that can provide full color in near darkness,
and more.

Advanced video analytics. Video analytics
have come a long way in terms of accuracy
and reliability. While motion detection
and cross-line detection have been staples of
border security for years, facial recognition
is starting to gain traction as another force
multiplier.

Fueled by advances in artificial intelligence
and machine learning, the software
measures distinguishing characteristics of a
face—such as the distance between the eyes,
skin tone, hairline, etc.—and cross-references
them against a database of photographs
collected by federal, state and local law enforcement
agencies. While countries like China
are using the technology to monitor and
influence the social behavior of their citizens,
U.S. agencies are using facial recognition to
augment screening at checkpoints. Considering
the complexity of border crossing—the
need to verify country clearances, check
for contraband and simultaneously identify
individuals on a watch list—introducing
technology that can reliably match an image
to a person of interest has the potential to
greatly reduce the opportunity for human
error while allowing those who are properly
vetted to move through checkpoints more efficiently.

Introducing Biometrics
into the Mix

U.S. Customs is already testing the efficacy
of other security tools like fingerprint
scanning and hand geometry imaging to
verify individuals pre-approved for entry
into the country. In order to expedite passage
at ports of entry, programs like GOES
(Global Online Enrollment System for U.S.
Customs and Border Protection Trusted
Traveler Program) along with its Canadian
counterpart NEXUS are applying emerging
technologies ranging from IDs embedded
with RFID chips to iris scans and other biometrics.
It’s a dual authentication procedure
that links passport documents with other
markers unique to the individual. Employing
these new screening technologies allow
border agents to concentrate the bulk of
their time on potentially higher-risk travelers
and goods.

Of course, agents still conduct random
screening interviews, but using technology
brings a whole new level of efficiency to
checkpoint processing. Additionally, there
are emerging technologies that can examine
a person’s facial expression and detect “sentiment” or mood. These types of incredible
advances have only recently been enabled by
similar advances in computing.

Adding Artificial
Intelligence and
Machine Learning

The next step in the evolution of border security
will likely be through artificial intelligence
(AI). In the not too distant future, introducing
AI into the security technologies used at the
borders will enable agents and operators to
accomplish specific tasks and make autonomous
decisions as well as, or ideally better
than humans. Bloggers are already noting the
AI has permeated the tech world on so many
fronts. It’s powering our virtual assistants, directing
our recommended playlists on media
streaming services, using image recognition
to suggest tags when we upload snapshots to
social media, etc. Eventually it will transform
the ways we interact with technology on a
fundamental level.

The ability to achieve human or superhuman
performance with AI-enhanced technology
rests with the underlying building
blocks of machine learning. At a basic level,
machine learning is the practice of using algorithms
to parse data, learn from it, and
then make determinations or predictions. In
other words, mimic human intelligence, but
at a scale not achievable by human physiology.
What really fuels machine learning, and
ultimately AI, is access to data—preferably
huge amounts of data. Thanks to the emergence
of the Internet, with its vast repository
of information and similarly, the migration
from analog to digital technology and the
seamless integration of networked security
technologies into a comprehensive physical
security information system, there’s plenty
of data to draw on and learn.

For DHS advanced machine learning algorithms
will eventually help to expand the
capabilities and extend the value and ROI
of technologies deployed on our southern
and northern frontiers. AI-enhanced technologies
will be able to selectively harvest
data and apply it against problem sets called
TTPs (Tactics, Techniques and Procedures)
which are constantly evolving. Having the
ability to ingest vast amounts of data and
develop predictive intelligence will make it
possible for border agents to make the critical,
split-second decisions fundamental to
the protection of our national security. And
that’s how U.S. border agencies will ultimately
create its own legendary
defensive line.

This article originally appeared in the October 2017 issue of Security Today.